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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Health Economicsarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Health Economics
Article . 2011 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
Health Economics
Article . 2013
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SPOILT FOR CHOICE: IMPLICATIONS OF USING ALTERNATIVE METHODS OF COSTING HOSPITAL EPISODE STATISTICS

Authors: Claudia, Geue; James, Lewsey; Paula, Lorgelly; Lindsay, Govan; Carole, Hart; Andrew, Briggs;

SPOILT FOR CHOICE: IMPLICATIONS OF USING ALTERNATIVE METHODS OF COSTING HOSPITAL EPISODE STATISTICS

Abstract

SUMMARYIn the absence of a ‘gold standard’ to estimate the economic burden of disease, a decision about the most appropriate costing method is required. Researchers have employed various methods to cost hospital stays, including per diem or diagnosis‐related group (DRG)‐based costs. Alternative methods differ in data collection and costing methodology. Using data from Scotland as an illustrative example, costing methods are compared, highlighting the wider implications for other countries with a publicly financed healthcare system.Five methods are compared using longitudinal data including baseline survey data (Midspan) linked to acute hospital admissions. Cost variables are derived using two forms of DRG‐type costs, costs per diem, costs per episode—using a novel approach that distinguishes between variable and fixed costs and incorporates individual length of stay (LOS), and costs per episode using national average LOS. Cost estimates are generated using generalised linear model regression.Descriptive analysis shows substantial variation between costing methods. Differences found in regression analyses highlight the magnitude of variation in cost estimates for subgroups of the sample population.This paper emphasises that any inference made from econometric modelling of costs, where the marginal effect of explanatory variables is assessed, is substantially influenced by the costing method. Copyright © 2011 John Wiley & Sons, Ltd.

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Keywords

Male, Data Collection, Age Factors, Length of Stay, Middle Aged, Hospital Charges, Hospitalization, Models, Economic, Sex Factors, Scotland, Socioeconomic Factors, Costs and Cost Analysis, Humans, Regression Analysis, Female, Longitudinal Studies, Diagnosis-Related Groups, Aged

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
39
Top 10%
Top 10%
Top 10%
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